The underrepresentation of palliative care in global guidelines for responding to infectious disease outbreaks: a systematic narrative review.

BACKGROUND: The importance of palliative care provision has been highlighted in previous humanitarian emergencies. This review aimed to examine the breadth and depth of palliative care inclusion within global guidelines for responding to infectious disease outbreaks. METHODS: The review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. Electronic searches of MEDLINE, Embase, Cumulative Index to Nursing and Allied Health, PsychInfo and grey literature were performed. Inclusion criteria were guidelines (recommendations for clinical practice or public health policy) for responding to infectious disease outbreaks in the general adult population. Results were limited to the English language, between 1 January 2010 and 17 August 2020. Analysis of the included articles involved assessing the breadth (number of palliative care domains covered) and depth (detail with which the domains were addressed) of palliative care inclusion. RESULTS: A total of 584 articles were retrieved and 43 met the inclusion criteria. Two additional articles were identified through handsearching. There was limited inclusion of palliative care in the guidelines examined. CONCLUSIONS: There is an opportunity for the development of guidelines that include information on palliative care implementation in the context of infectious disease outbreaks in order to reduce the suffering of key vulnerable populations worldwide.

COVID-19 Supply Chain Planning: A Simulation-Optimization Approach

Healthcare providers' preparedness and response plans are crucial to effectively cope with infectious disease outbreaks such as COVID-19. These plans need to provide strategic and operational actionable insights to guarantee the availability of essential resources when needed. This study uses a simulation-optimization approach to (i) determine an optimal replenishment policy to restock personal protective equipment (PPE) items, and (ii) determine proactive demand planning for critical resources such as the number of beds, and ventilators. This model leverages a Simio-MATLAB integration to complete simulation and optimization tasks. © 2022 IEEE.

Analyzing Chest X-Ray to Detect the Evidence of Lung Abnormality Due to Infectious Disease

Analyzing chest X-ray is the must especially when are required to deal of infectious disease outbreak, and COVID-19. The COVID-19 pandemic has had a large effect on almost every facet of life. As COVID-19 was a disease only discovered in recent history, there is comparatively little data on the disease, how it is detected, and how it is cured. Deep learning is a powerful tool that can be used to learn to classify information in ways that humans might not be able to. This allows computers to learn on relatively little data and provide exceptional results. This paper proposes a customized convolutional neural network (CNN) for the detection of COVID-19 from chest X-rays called basicConv. This network consists of five sets of convolution and pooling layers, a flatten layer, and two dense layers with a total of approximately 9 million parameters. This network achieves an accuracy of 95.8%, which is comparable to other high-performing image classification networks. This provides a promising launching point for future research and developing a network that achieves an accuracy higher than that of the leading classification networks. It also demonstrates the incredible power of convolution. This paper is an extension of a 2022 Honors Thesis (Henderson, Joshua Elliot, "Convolutional Neural Network for COVID-19 Detection in Chest X-Rays” (2022). Honors Thesis. 254. https://red.library.usd.edu/honors-thesis/254 ). © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Contact Tracing Platform in OSN for Prevention of Infectious Disease Outbreaks

To limit the spread of COVID-19, social distancing measurements and contact tracing have become popular strategies implemented worldwide. In addition to manual contact tracing, smartphone-based applications based on proximity detection have emerged to speed up the discovery of potential infectious individuals. However, so far, their effectiveness has been limited, mainly due to privacy issues. A new tracing mechanism is represented by Online Social Networks (OSNs), which provide a successful way to track, share and exchange information in real-time. Being extremely popular and largely used by citizens, OSNs are less exposed to privacy concerns. In this paper, we present an OSN-based contact tracing platform called TraceMe to reduce the spread of the epidemic. The proposal currently targets COVID-19, but it can be used in presence of other infectious diseases, like Ebola, swine flue, etc. TraceMe implements conventional contact tracing based on physical proximity and, in addition, it leverages OSNs to identify other contacts potentially exposed to the virus. To efficiently find the targeted social community, while saving the time complexity, a clique-based method is applied. Performance evaluation based on a realistic dataset shows that TraceMe is able to analyse large-scale social networks in order to find, and then alert, the tight communities of contacts that are at high risk of infection. © 2023 IEEE.

Transmission potential of vaccinated and unvaccinated persons infected with the SARS-CoV-2 Delta variant in a federal prison, July-August 2021.

BACKGROUND: The extent to which vaccinated persons who become infected with SARS-CoV-2 contribute to transmission is unclear. During a SARS-CoV-2 Delta variant outbreak among incarcerated persons with high vaccination rates in a federal prison, we assessed markers of viral shedding in vaccinated and unvaccinated persons. METHODS: Consenting incarcerated persons with confirmed SARS-CoV-2 infection provided mid-turbinate nasal specimens daily for 10 consecutive days and reported symptom data via questionnaire. Real-time reverse transcription-polymerase chain reaction (RT-PCR), viral whole genome sequencing, and viral culture was performed on these nasal specimens. Duration of RT-PCR positivity and viral culture positivity was assessed using survival analysis. RESULTS: A total of 957 specimens were provided by 93 participants, of whom 78 (84 %) were vaccinated and 17 (16 %) were unvaccinated. No significant differences were detected in duration of RT-PCR positivity among vaccinated participants (median: 13 days) versus those unvaccinated (median: 13 days; p = 0.50), or in duration of culture positivity (medians: 5 days and 5 days; p = 0.29). Among vaccinated participants, overall duration of culture positivity was shorter among Moderna vaccine recipients versus Pfizer (p = 0.048) or Janssen (p = 0.003) vaccine recipients. In post-hoc analyses, Moderna vaccine recipients demonstrated significantly shorter duration of culture positivity compared to unvaccinated participants (p = 0.02). When restricted to participants without reported prior infection, the difference between Moderna vaccine recipients and unvaccinated participants was more pronounced (medians: 3 days and 6 days, p = 0.002). CONCLUSIONS: Infectious periods for vaccinated and unvaccinated persons who become infected with SARS-CoV-2 are similar and can be highly variable, though some vaccinated persons are likely infectious for shorter durations. These findings are critically important, especially in congregate settings where viral transmission can lead to large outbreaks. In such settings, clinicians and public health practitioners should consider vaccinated, infected persons to be no less infectious than unvaccinated, infected persons.

Pre-pandemic Social Integration: Protection or Risk for Older Adults in the Time of COVID-19?

OBJECTIVES: To examine the association between pre-pandemic social integration and post-traumatic stress disorder (PTSD) symptoms during the COVID-19 pandemic and test whether the association is mediated by social support received and social events missed during the pandemic. We also explored age, race, gender, and socioeconomic differences in the association. METHODS: We adopted a prospective design. Path analysis was conducted using data from the COVID-19 supplement (2020) and the 2019 wave of the National Health and Aging Trends Study. The sample represents Medicare beneficiaries age 70+ (N = 2,694). Social integration was measured using a 6-item index. A 6-item standardized scale assessed PTSD symptoms. Both social support received and social events missed were single-item measures. The analysis controlled for sociodemographic characteristics, pre-pandemic physical and mental health, and coronavirus exposure during the pandemic. RESULTS: Pre-pandemic social integration was positively associated with PTSD symptoms during the pandemic. The association was primarily mediated by social events missed- high levels of social integration were associated with missing more social events during the pandemic resulting in more PTSD symptoms. Social support received was also a mediator-social integration was positively associated with social support received during the pandemic, with more received support associated with greater PTSD symptoms. Pre-pandemic social integration had no significant direct effect on PTSD symptoms. The direct, indirect, and total effects of social integration on PTSD symptoms did not significantly differ by age, race, gender, education or poverty status. DISCUSSION: Social integration may carry mental health risks in times of infectious disease outbreaks.

Outbreak investigation in a COVID-19 designated hospital: The combination of phylogenetic analysis and field epidemiology study suggesting airborne transmission.

BACKGROUND: Healthcare-associated COVID-19 infections caused by SARS-CoV-2 have increased morbidity and mortality. Hospitals and skilled nursing facilities (SNFs) have been challenged by infection control and management. METHODS: This case study presents an outbreak investigation in a COVID-19-designated hospital and a hospital-based SNF. Real-time polymerase chain reaction (PCR) and other studies were performed on samples obtained from SNF residents, hospital patients, and healthcare workers (HCWs). The results of the laboratory tests and field epidemiological data were analyzed. Genome sequencing and phylogenetic analysis of SARS-CoV-2 were performed to identify the associations between cases. The tracer gas was released and recorded by a thermal imaging camera to investigate the spatial relations within clusters. RESULTS: During the outbreak, 29 COVID-19 infections in 3 clusters were identified through hospital-wide, risk-guided, and symptom-driven PCR tests. This included 12 HCWs, 5 patients, and 12 SNF residents who had been hospitalized for at least 14 days. Serology tests did not identify any cases among the PCR-negative individuals. The phylogenetic analysis revealed that viral strains from the 3 clusters shared a common mutation of G3994T and were phylogenetically related, which suggested that this outbreak had a common source rather than multiple introductions from the community. Linked cases exhibited vertical spatial distribution, and the sulfur hexafluoride release test confirmed a potential airborne transmission. CONCLUSIONS: This report addressed the advantage of a multi-disciplinary team in outbreak investigation. Identifying an airborne transmission within an outbreak highlighted the importance of regular maintenance of ventilation systems.

Associations of the COVID-19 pandemic with the reported incidence of important endemic infectious disease agents and syndromes in Pakistan.

BACKGROUND: Persons in Pakistan have suffered from various infectious diseases over the years, each impacted by various factors including climate change, seasonality, geopolitics, and resource availability. The COVID-19 pandemic is another complicating factor, with changes in the reported incidence of endemic infectious diseases and related syndromes under surveillance. METHODS: We assessed the monthly incidence of eight important infectious diseases/syndromes: acute upper respiratory infection (AURI), viral hepatitis, malaria, pneumonia, diarrhea, typhoid fever, measles, and neonatal tetanus (NNT), before and after the onset of the COVID-19 pandemic. Administrative health data of monthly reported cases of these diseases/syndromes from all five provinces/regions of Pakistan for a 3-year interval (March 2018-February 2021) were analyzed using an interrupted time series approach. Reported monthly incidence for each infectious disease agent or syndrome and COVID-19 were subjected to time series visualization. Spearman's rank correlation coefficient between each infectious disease/syndrome and COVID-19 was calculated and median case numbers of each disease before and after the onset of the COVID-19 pandemic were compared using a Wilcoxon signed-rank test. Subsequently, a generalized linear negative binomial regression model was developed to determine the association between reported cases of each disease and COVID-19. RESULTS: In late February 2020, concurrent with the start of COVID-19, in all provinces, there were decreases in the reported incidence of the following diseases: AURI, pneumonia, hepatitis, diarrhea, typhoid, and measles. In contrast, the incidence of COVID was negatively associated with the reported incidence of NNT only in Punjab and Sindh, but not in Khyber Pakhtunkhwa (KPK), Balochistan, or Azad Jammu & Kashmir (AJK) & Gilgit Baltistan (GB). Similarly, COVID-19 was associated with a lowered incidence of malaria in Punjab, Sindh, and AJK & GB, but not in KPK and Balochistan. CONCLUSIONS: COVID-19 was associated with a decreased reported incidence of most infectious diseases/syndromes studied in most provinces of Pakistan. However, exceptions included NNT in KPK, Balochistan and AJK & GB, and malaria in KPK and Balochistan. This general trend was attributed to a combination of resource diversion, misdiagnosis, misclassification, misinformation, and seasonal patterns of each disease.

Willingness to pay for health gains from an international integrated early warning system for infectious disease outbreaks.

Recently, due to the corona virus outbreak, pandemics and their effects have been at the forefront of the research agenda. However, estimates of the perceived value of early warning systems (EWSs) for identifying, containing, and mitigating outbreaks remain scarce. This paper aims to show how potential health gains due to an international EWS might be valued. This paper reports on a study into willingness to pay (WTP) in six European countries for health gains due to an EWS. The context in which health is gained, those affected, and the reduction in risk of contracting the disease generated by the EWS are varied across seven scenarios. Using linear regression, we analyse this 'augmented' willingness to pay for a QALY (WTP-Q) for each of the scenarios, where 'augmented' refers to the possible inclusion of context specific elements of value, such as feelings of safety. An initial WTP-Q estimate for the basic scenario is €17,400. This can be interpreted as a threshold for investment per QALY into an EWS. Overall, WTP estimates move in the expected directions (e.g. higher risk reduction leads to higher WTP). However, changes in respondents' WTP for reductions in risk were not proportional to the magnitude of the change in risk reduction. This study provided estimates of the monetary value of health gains in the context of a pandemic under seven scenarios which differ in terms of outcome, risk reduction and those affected. It also highlights the importance of future research into optimal ways of eliciting thresholds for investments in public health interventions.

Sequential Vaccine Allocation with Delayed Feedback

In this work we consider the problem of how to best allocate a limited supply of vaccines in the aftermath of an infectious disease outbreak by viewing the problem as a sequential game between a learner and an environment (specifically, a bandit problem). The difficulty of this problem lies in the fact that the payoff of vaccination cannot be directly observed, making it difficult to compare the relative effectiveness of vaccination on different population groups. Currently used vaccination policies make recommendations based on mathematical modelling and ethical considerations. These policies are static, and do not adapt as conditions change. Our aim is to design and evaluate an algorithm which can make use of routine surveillance data to dynamically adjust its recommendation. We evaluate the performance of our approach by applying it to a simulated epidemic of a disease based on real-world COVID-19 data, and show that our vaccination policy was able to perform better than existing vaccine allocation policies. In particular, we show that with our allocation method, we can reduce the number of required vaccination by at least 50% in order to keep the peak number of hospitalised patients below a certain threshold. Also, when the same batch sizes are used, our method can reduce the peak number of hospitalisation by up to 20%. We also demonstrate that our vaccine allocation does not vary the number of batches per group much, making it socially more acceptable (as it reduces uncertainty, hence results in better and more interpretable communication). © 2022 International Joint Conferences on Artificial Intelligence. All rights reserved.

Detection of Infectious Disease Outbreaks in Search Engine Time Series Using Non-Specific Syndromic Surveillance with Effect-Size Filtering

Novel infectious disease outbreaks, including most recently that of the COVID-19 pandemic, could be detected by non-specific syndromic surveillance systems. Such systems, utilizing a variety of data sources ranging from Electronic Health Records to internet data such as aggregated search engine queries, create alerts when unusually high rates of symptom reports occur. This is especially important for the detection of novel diseases, where their manifested symptoms are unknown. Here we improve upon a set of previously-proposed non-specific syndromic surveillance methods by taking into account both how unusual a preponderance of symptoms is and their effect size. We demonstrate that our method is as accurate as previously-proposed methods for low dimensional data and show its effectiveness for high-dimensional aggregated data by applying it to aggregated time-series health-related search engine queries. We find that in 2019 the method would have raised alerts related to several disease outbreaks earlier than health authorities did. During the COVID-19 pandemic the system identified the beginning of pandemic waves quickly, through combinations of symptoms which varied from wave to wave. Thus, the proposed method could be used as a practical tool for decision makers to detect new disease outbreaks using time series derived from search engine data even in the absence of specific information on the diseases of interest and their symptoms. © 2022 ACM.

Challenges and Opportunities for Mental Health and Psychosocial Support in the COVID-19 Response in Africa: A Mixed-Methods Study.

This research aimed to (1) assess the extent to which mental health and psycho-social support (MHPSS) was included in the national response to the COVID-19 pandemic in African countries, and (2) explore barriers and enablers to MHPSS integration into the COVID-19 response. A mixed-methods study, using an online survey and in-depth interviews, was conducted. Participants included Mental Health Focal Points at the Ministries of Health, the World Health Organization (WHO) country and regional offices, and civil society representatives. Responses were received from 28 countries out of 55 contacted. The implementation level, based on standard guidelines, of MHPSS activities was below 50% in most countries. The most implemented MHPSS activities were establishing coordination groups (57%) and developing MHPSS strategy (45%), while the least implemented activities included implementing the developed MHPSS strategy (32%) and establishing monitoring and evaluation mechanisms (21%). Key factors that hindered implementing MHPSS activities included lack of political commitment and low prioritisation of mental health during emergencies, as it was seen as a "less important" issue during the COVID-19 pandemic, when more importance was given to infection prevention and control (IPC). However, there are signs of optimism, as mental health gained some attention during COVID-19. It is imperative to build on the attention gained by integrating MHPSS in emergency preparedness and response and strengthening mental health systems in the longer term.

Public health emergencies: Case studies, competencies, and essential services of public health

Public Health Emergencies provides a current overview of public health emergency preparedness and response principles with case studies highlighting lessons learned from recent natural and man-made disasters and emergencies. Designed for graduate and advanced undergraduate public health students, this book utilizes the 10 essential services of public health as performance standards and foundational competencies from the Council on Education for Public Health to assess public health systems. It emphasizes the roles and responsibilities of public health careers in state and local health departments as well as other institutions and clarifies their importance during health-related emergencies in the community. Written by prominent experts, including health professionals and leaders on the frontlines, this textbook provides the framework and lessons for understanding the public health implications of disasters, emergencies, and other catastrophic events, stressing applied understanding for students interested in pursuing public health preparedness roles. Practical in its approach, Part One begins with an introduction to the fundamentals of public health emergency preparedness with chapters on community readiness, all-hazards preparedness design, disaster risk assessments, and emergency operation plans. Part Two covers a range of public health emergency events, including hurricanes, tornadoes, earthquakes, disease outbreaks and pandemics, accidents and chemical contamination, nuclear and radiological hazards, extreme heat events, and water supply hazards. The final part addresses special considerations, such as how the law serves as a foundation to public health actions;preparedness considerations for persons with disabilities, access, and functional needs;children and disasters;and a chapter evaluating emerging and evolving threats. Throughout, chapters convey the roles of front-line, supervisory, and leadership personnel of the many stakeholders involved in preparedness, response, and recovery efforts to demonstrate decision-making in action. © 2022 Springer Publishing Company, LLC.

The impact of organisational characteristics of staff and facility on infectious disease outbreaks in care homes: a systematic review.

BACKGROUND: Infectious disease outbreaks are common in care homes, often with substantial impact on the rates of infection and mortality of the residents, who primarily are older people vulnerable to infections. There is growing evidence that organisational characteristics of staff and facility might play a role in infectious disease outbreaks however such evidence have not previously been systematically reviewed. Therefore, this systematic review aims to examine the impact of facility and staff characteristics on the risk of infectious disease outbreaks in care homes. METHODS: Five databases (MEDLINE, EMBASE, ProQuest, Web of Science, CINAHL) were searched. Studies considered for inclusion were of any design reporting on an outbreak of any infectious disease in one or more care homes providing care for primarily older people with original data on: facility size, facility location (urban/rural), facility design, use of temporary hired staff, staff compartmentalizing, residence of staff, and/or nursing aides hours per resident. Retrieved studies were screened, assessed for quality using CASP, and analysed employing a narrative synthesis. RESULTS: Sixteen studies (8 cohort studies, 6 cross-sectional studies, 2 case-control) were included from the search which generated 10,424 unique records. COVID-19 was the most commonly reported cause of outbreak (n = 11). The other studies focused on influenza, respiratory and gastrointestinal outbreaks. Most studies reported on the impact of facility size (n = 11) followed by facility design (n = 4), use of temporary hired staff (n = 3), facility location (n = 2), staff compartmentalizing (n = 2), nurse aides hours (n = 2) and residence of staff (n = 1). Findings suggest that urban location and larger facility size may be associated with greater risks of an infectious disease outbreak. Additionally, the risk of a larger outbreak seems lower in larger facilities. Whilst staff compartmentalizing may be associated with lower risk of an outbreak, staff residing in highly infected areas may be associated with greater risk of outbreak. The influence of facility design, use of temporary staff, and nurse aides hours remains unclear. CONCLUSIONS: This systematic review suggests that larger facilities have greater risks of infectious disease outbreaks, yet the risk of a larger outbreak seems lower in larger facilities. Due to lack of robust findings the impact of facility and staff characteristics on infectious disease outbreaks remain largely unknown. PROSPERO: CRD42020213585 .

Time Series Analysis of Infected COVID-19 Cases in the Zamboanga Peninsula, Philippines using Long Short-Term Memory Neural Networks

Infectious disease outbreaks, such as COVID-19 pandemics, exhibit patterns that can be described by the dynamics of a mathematical model This study seeks to explore the use of LSTM in order to develop models that will capture the non-linear dynamic changes of COVID-19 cases in Zamboanga Peninsula. The study uses 436 data points where the latest timestamp for the dataset is on May 29, 2021 and the oldest is on March 20, 2020. These data are taken from the DOH repositories and revalidated using the data from the DOH Regional Office. The training and testing phase results show that among the different LSTM variants, convLSTM trained using Adam and RMSProp attained the smallest RMSE result of 42.34 and 43.67 and a correlation coefficient of 0.94 0.93, respectively. ConvLSTM, when trained with Adam and RMSProp, produces the best results, as evidenced by the shortest RMSE and highest correlation coefficient. Results revealed that convLSTM appears to be a viable choice for modeling the time series of the COVID 19 infected cases in Zamboanga Peninsula Region in compared with the different variants of LSTM. © 2021 IEEE.

Modeling the containment behavior of interacting populations in response to an epidemic

Epidemic disease outbreaks are among the major threats to the sustenance and health of human societies, as evidenced by the crises caused by the COVID-19 pandemic. Many people have lost their lives because of this pandemic, and the impact of it on the global economy has also been severe. Modeling the infectious disease outbreak in search of the set of optimal strategies to control the epidemics can help the public health policy makers to better decide and design relevant policies. In this study, spatial games under public goods policies are used to model the social response of different interacting populations to a new epidemic, where the decision makers are not individuals but societies. This approach is of great importance for policy evaluation, since there are usually not just individuals who decide to change their behavior in response to an epidemic, but societies who affect the change of individuals behaviors by setting relevant health policies, standards and regulations. © 2021 IISE Annual Conference and Expo 2021. All rights reserved.

Coronavirus disease 2019 (COVID-19): update for anesthesiologists and intensivists March 2020.

The current outbreak of coronavirus disease (COVID-19) has reached Germany. The majority of people infected present with mild disease, but there are severe cases that need intensive care. Unlike other acute infectious diseases progressing to sepsis, the severe courses of COVID19 seemingly show prolonged progression from onset of first symptoms to life-threatening deterioration of (primarily) lung function. Diagnosis relies on PCR using specimens from the respiratory tract. Severe ARDS reflects the hallmark of a critical course of the disease. Preventing nosocomial infections (primarily by correct use of personal protective equipment) and maintenance of hospitals' operational capability are of utmost importance. Departments of Anaesthesia, Intensive Care and emergency medicine will envisage major challenges.

Statistical Estimation of the Reproductive Number From Case Notification Data.

The reproductive number, or reproduction number, is a valuable metric in understanding infectious disease dynamics. There is a large body of literature related to its use and estimation. In the last 15 years, there has been tremendous progress in statistically estimating this number using case notification data. These approaches are appealing because they are relevant in an ongoing outbreak (e.g., for assessing the effectiveness of interventions) and do not require substantial modeling expertise to be implemented. In this article, we describe these methods and the extensions that have been developed. We provide insight into the distinct interpretations of the estimators proposed and provide real data examples to illustrate how they are implemented. Finally, we conclude with a discussion of available software and opportunities for future development.

Willingness to Donate Remnant Human Biospecimens in the Context of the COVID-19 Pandemic.

Background: Studies using biospecimens can help reveal pathogenic mechanisms and improve prevention, diagnosis, and treatment of diseases. However, there is still a lack of relevant investigation data, which can provide initial evidence for establishing or improving relevant laws and regulations, on people's willingness to donate biospecimens, and whether they agree to waive the right of obtaining informed consent in the special period of sudden outbreak of new infectious diseases. Objectives: To investigate people's willingness to donate their remnant biospecimens of clinical tests for research in the context of the COVID-19 pandemic and their willingness to sign the informed consent for research using their biospecimens. Methods: We conducted a survey using an online questionnaire, which included questions on basic personal information, COVID-19-related information, donation of remnant biospecimens, willingness to sign informed consent, and reasons to do so. Results: Among the 721 valid responses, 620 respondents (86.0%) reported that they would be willing to donate their remnant biospecimens for research, of whom 434 (70.0%) reported that they would donate their remnant biospecimens without signing the informed consent. Of the 11 specified influencing factors, occupation, household income, and degree of concern about the COVID-19 pandemic were associated with willingness to donate remnant biospecimens. Gender and age were associated with willingness to donate remnant biospecimens without signing the informed consent. The main reasons for unwillingness to donate remnant biospecimens and sign the informed consent were a limited knowledge of research and privacy concerns. Conclusions: Most respondents reported that they would be willing to donate their remnant biospecimens for biomedical research without signing an informed consent in the context of the COVID-19 pandemic. Lack of understanding of the proposed research and concerns about personal privacy were the main reasons for unwillingness to donate biospecimens and signing the informed consent.